train.py

import os

import torch
import torchvision.transforms as transforms
from loss import TripletMarginLoss
from model import EmbeddingNet
from sampler import PKSampler
from torch.optim import Adam
from torch.utils.data import DataLoader
from torchvision.datasets import FashionMNIST


def train_epoch(model, optimizer, criterion, data_loader, device, epoch, print_freq):
    model.train()
    running_loss = 0
    running_frac_pos_triplets = 0
    for i, data in enumerate(data_loader):
        optimizer.zero_grad()
        samples, targets = data[0].to(device), data[1].to(device)

        embeddings = model(samples)

        loss, frac_pos_triplets = criterion(embeddings, targets)
        loss.backward()
        optimizer.step()

        running_loss += loss.item()
        running_frac_pos_triplets += float(frac_pos_triplets)

        if i % print_freq == print_freq - 1:
            i += 1
            avg_loss = running_loss / print_freq
            avg_trip = 100.0 * running_frac_pos_triplets / print_freq
            print(f"[{epoch:d}, {i:d}] | loss: {avg_loss:.4f} | % avg hard triplets: {avg_trip:.2f}%")
            running_loss = 0
            running_frac_pos_triplets = 0


def find_best_threshold(dists, targets, device):
    best_thresh = 0.01
    best_correct = 0
    for thresh in torch.arange(0.0, 1.51, 0.01):
        predictions = dists <= thresh.to(device)
        correct = torch.sum(predictions == targets.to(device)).item()
        if correct > best_correct:
            best_thresh = thresh
            best_correct = correct

    accuracy = 100.0 * best_correct / dists.size(0)

    return best_thresh, accuracy


@torch.inference_mode()
def evaluate(model, loader, device):
    model.eval()
    embeds, labels = [], []
    dists, targets = None, None

    for data in loader:
        samples, _labels = data[0].to(device), data[1]
        out = model(samples)
        embeds.append(out)
        labels.append(_labels)

    embeds = torch.cat(embeds, dim=0)
    labels = torch.cat(labels, dim=0)

    dists = torch.cdist(embeds, embeds)

    labels = labels.unsqueeze(0)
    targets = labels == labels.t()

    mask = torch.ones(dists.size()).triu() - torch.eye(dists.size(0))
    dists = dists[mask == 1]
    targets = targets[mask == 1]

    threshold, accuracy = find_best_threshold(dists, targets, device)

    print(f"accuracy: {accuracy:.3f}%, threshold: {threshold:.2f}")


def save(model, epoch, save_dir, file_name):
    file_name = "epoch_" + str(epoch) + "__" + file_name
    save_path = os.path.join(save_dir, file_name)
    torch.save(model.state_dict(), save_path)


def main(args):
    device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
    p = args.labels_per_batch
    k = args.samples_per_label
    batch_size = p * k

    model = EmbeddingNet()
    if args.resume:
        model.load_state_dict(torch.load(args.resume))

    model.to(device)

    criterion = TripletMarginLoss(margin=args.margin)
    optimizer = Adam(model.parameters(), lr=args.lr)

    transform = transforms.Compose(
        [transforms.Lambda(lambda image: image.convert("RGB")), transforms.Resize((224, 224)), transforms.ToTensor()]
    )

    # Using FMNIST to demonstrate embedding learning using triplet loss. This dataset can
    # be replaced with any classification dataset.
    train_dataset = FashionMNIST(args.dataset_dir, train=True, transform=transform, download=True)
    test_dataset = FashionMNIST(args.dataset_dir, train=False, transform=transform, download=True)

    # targets is a list where the i_th element corresponds to the label of i_th dataset element.
    # This is required for PKSampler to randomly sample from exactly p classes. You will need to
    # construct targets while building your dataset. Some datasets (such as ImageFolder) have a
    # targets attribute with the same format.
    targets = train_dataset.targets.tolist()

    train_loader = DataLoader(
        train_dataset, batch_size=batch_size, sampler=PKSampler(targets, p, k), num_workers=args.workers
    )
    test_loader = DataLoader(test_dataset, batch_size=args.eval_batch_size, shuffle=False, num_workers=args.workers)

    for epoch in range(1, args.epochs + 1):
        print("Training...")
        train_epoch(model, optimizer, criterion, train_loader, device, epoch, args.print_freq)

        print("Evaluating...")
        evaluate(model, test_loader, device)

        print("Saving...")
        save(model, epoch, args.save_dir, "ckpt.pth")


def parse_args():
    import argparse

    parser = argparse.ArgumentParser(description="PyTorch Embedding Learning")

    parser.add_argument("--dataset-dir", default="/tmp/fmnist/", type=str, help="FashionMNIST dataset directory path")
    parser.add_argument(
        "-p", "--labels-per-batch", default=8, type=int, help="Number of unique labels/classes per batch"
    )
    parser.add_argument("-k", "--samples-per-label", default=8, type=int, help="Number of samples per label in a batch")
    parser.add_argument("--eval-batch-size", default=512, type=int, help="batch size for evaluation")
    parser.add_argument("--epochs", default=10, type=int, metavar="N", help="number of total epochs to run")
    parser.add_argument("-j", "--workers", default=4, type=int, metavar="N", help="number of data loading workers")
    parser.add_argument("--lr", default=0.0001, type=float, help="initial learning rate")
    parser.add_argument("--margin", default=0.2, type=float, help="Triplet loss margin")
    parser.add_argument("--print-freq", default=20, type=int, help="print frequency")
    parser.add_argument("--save-dir", default=".", type=str, help="Model save directory")
    parser.add_argument("--resume", default="", type=str, help="path of checkpoint")

    return parser.parse_args()


if __name__ == "__main__":
    args = parse_args()
    main(args)